# Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation

arXiv: https://arxiv.org/abs/1708.03416 · HTML: https://mlsanity.com/paper/1708.03416/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 12 of 12)

- [HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton](https://mlsanity.com/paper/2108.05545/) (2021)
- [A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2304.03635/) (2023)
- [HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud](https://mlsanity.com/paper/2404.03159/) (2024)
- [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)
- [Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation](https://mlsanity.com/paper/2210.16788/) (2022)
- [Confidence Calibration for Systems with Cascaded Predictive Modules](https://mlsanity.com/paper/2309.12510/) (2023)
- [Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training](https://mlsanity.com/paper/2303.15147/) (2023)
- [HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation](https://mlsanity.com/paper/2407.20542/) (2024)
- [DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation](https://mlsanity.com/paper/2403.13405/) (2024)
- [HandMCM: Multi-modal Point Cloud-based Correspondence State Space Model for 3D Hand Pose Estimation](https://mlsanity.com/paper/2602.01586/) (2026)
- [KAD-Net: Kinematics-Aware Decoupled Learning for Robust 3D Hand Pose Estimation from a Single Depth Image](https://mlsanity.com/paper/2609.12559/) (2026)

## Abstract (arXiv)

Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.